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Liu J, Cundy TP, Woon DTS, Lawrentschuk N. A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans. Cancers (Basel) 2024; 16:486. [PMID: 38339239 PMCID: PMC10854940 DOI: 10.3390/cancers16030486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.
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Affiliation(s)
- Jianliang Liu
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Thomas P. Cundy
- Discipline of Surgery, University of Adelaide, Adelaide, SA 5005, Australia
| | - Dixon T. S. Woon
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Nathan Lawrentschuk
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
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Lindgren Belal S, Frantz S, Minarik D, Enqvist O, Wikström E, Edenbrandt L, Trägårdh E. Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging. Semin Nucl Med 2024; 54:141-149. [PMID: 37357026 DOI: 10.1053/j.semnuclmed.2023.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/27/2023]
Abstract
Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.
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Affiliation(s)
- Sarah Lindgren Belal
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Surgery, Skåne University Hospital, Malmö, Sweden
| | - Sophia Frantz
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Health Technology Assessment South, Skåne University Hospital, Lund, Sweden
| | - David Minarik
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Radiation Physics, Skåne University Hospital, Malmö, Sweden
| | - Olof Enqvist
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden; Department of Clinical Physiology and Nuclear Medicine, Malmö Sweden
| | - Erik Wikström
- Department of Health Technology Assessment South, Skåne University Hospital, Lund, Sweden
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Elin Trägårdh
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden.
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